#!/usr/bin/env python3
"""
Parameter behavior analysis for mosaic generator
Focuses on: parameter degradation effects, CIFAR comparison, cross-image consistency
Analyzes three images: akaza, IMG_5090, IMG_6914
"""

import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
from collections import defaultdict
import warnings
warnings.filterwarnings('ignore')

# Configuration
METRICS_DIR = Path('./output/test_result/metrics')
OUTPUT_DIR = Path('./output/analysis')
TARGET_IMAGES = ['akaza', 'IMG_3378', 'IMG_5090', 'IMG_6914']

# Visualization settings
plt.style.use('default')
sns.set_palette("husl")

def setup_directories():
    """Create analysis output directories"""
    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
    for image in TARGET_IMAGES:
        (OUTPUT_DIR / image).mkdir(parents=True, exist_ok=True)
    print(f"[INFO] Created analysis directories under {OUTPUT_DIR}")

def parse_filename(filename):
    """Parse filename to extract parameters"""
    # Pattern: {image}-min{min}-max{max}-sub{sub}-quant{quant}-{tile}_metrics.json
    pattern = r'(.+)-min(\d+)-max(\d+)-sub([0-9p]+)-quant(\d+)-(.+)_metrics\.json'
    match = re.match(pattern, filename)

    if not match:
        return None

    image, min_size, max_size, sub_threshold, quantization, tile_library = match.groups()

    # Convert threshold back from string (0p1 -> 0.1)
    sub_threshold = sub_threshold.replace('p', '.')

    return {
        'image': image,
        'min_size': int(min_size),
        'max_size': int(max_size),
        'sub_threshold': float(sub_threshold),
        'quantization': int(quantization),
        'tile_library': tile_library
    }

def load_metrics_data():
    """Load all metrics data and organize by image"""
    data_by_image = defaultdict(list)

    print("[INFO] Loading metrics data...")

    for json_file in METRICS_DIR.glob('*.json'):
        # Skip files not belonging to our target images
        if not any(img in json_file.name for img in TARGET_IMAGES):
            continue

        parsed = parse_filename(json_file.name)
        if parsed is None:
            continue

        # Load metrics
        try:
            with open(json_file, 'r') as f:
                metrics = json.load(f)

            # Combine parameters and metrics
            record = {**parsed, **metrics}
            data_by_image[parsed['image']].append(record)

        except Exception as e:
            print(f"[WARNING] Failed to load {json_file.name}: {e}")

    # Convert to DataFrames
    dfs = {}
    for image, records in data_by_image.items():
        df = pd.DataFrame(records)
        dfs[image] = df
        print(f"[INFO] Loaded {len(df)} records for {image}")

    return dfs

def create_heatmaps(df, image_name):
    """Create heatmap visualizations for an image"""
    print(f"[INFO] Creating heatmaps for {image_name}")

    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle(f'Parameter Impact Heatmaps - {image_name}', fontsize=16, fontweight='bold')

    # 1. Min_size vs Max_size colored by SSIM
    pivot1 = df.pivot_table(values='SSIM', index='min_size', columns='max_size', aggfunc='mean')
    sns.heatmap(pivot1, annot=True, fmt='.3f', ax=axes[0,0], cmap='RdYlGn')
    axes[0,0].set_title('SSIM by Min/Max Size')
    axes[0,0].set_xlabel('Max Size')
    axes[0,0].set_ylabel('Min Size')

    # 2. Quantization vs Tile_library colored by Overall_Quality
    pivot2 = df.pivot_table(values='Overall_Quality', index='quantization', columns='tile_library', aggfunc='mean')
    sns.heatmap(pivot2, annot=True, fmt='.3f', ax=axes[0,1], cmap='RdYlGn')
    axes[0,1].set_title('Overall Quality by Quantization/Tile Library')
    axes[0,1].set_xlabel('Tile Library')
    axes[0,1].set_ylabel('Quantization')

    # 3. Subdivision vs Quantization colored by MSE (inverted colormap since lower MSE is better)
    pivot3 = df.pivot_table(values='MSE', index='sub_threshold', columns='quantization', aggfunc='mean')
    sns.heatmap(pivot3, annot=True, fmt='.0f', ax=axes[1,0], cmap='RdYlGn_r')
    axes[1,0].set_title('MSE by Subdivision/Quantization')
    axes[1,0].set_xlabel('Quantization')
    axes[1,0].set_ylabel('Subdivision Threshold')

    # 4. Tile library vs Min_size colored by Edge_Similarity
    pivot4 = df.pivot_table(values='Edge_Similarity', index='tile_library', columns='min_size', aggfunc='mean')
    sns.heatmap(pivot4, annot=True, fmt='.3f', ax=axes[1,1], cmap='RdYlGn')
    axes[1,1].set_title('Edge Similarity by Tile Library/Min Size')
    axes[1,1].set_xlabel('Min Size')
    axes[1,1].set_ylabel('Tile Library')

    plt.tight_layout()
    output_path = OUTPUT_DIR / image_name / 'heatmaps.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved heatmaps to {output_path}")

def create_metric_comparisons(df, image_name):
    """Create metric comparison charts"""
    print(f"[INFO] Creating metric comparisons for {image_name}")

    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle(f'Metric Comparisons - {image_name}', fontsize=16, fontweight='bold')

    # 1. Box plots by tile library
    metrics_to_plot = ['SSIM', 'Histogram_Correlation', 'Edge_Similarity', 'Overall_Quality']

    for i, metric in enumerate(metrics_to_plot):
        ax = axes[i//2, i%2]
        df.boxplot(column=metric, by='tile_library', ax=ax)
        ax.set_title(f'{metric} by Tile Library')
        ax.set_xlabel('Tile Library')
        ax.set_ylabel(metric)
        ax.grid(True, alpha=0.3)

    plt.suptitle('')  # Remove automatic title
    fig.suptitle(f'Metric Distribution by Tile Library - {image_name}', fontsize=16, fontweight='bold')
    plt.tight_layout()

    output_path = OUTPUT_DIR / image_name / 'metric_comparison.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved metric comparison to {output_path}")

def create_parameter_impact(df, image_name):
    """Create parameter impact analysis"""
    print(f"[INFO] Creating parameter impact analysis for {image_name}")

    fig, axes = plt.subplots(2, 3, figsize=(18, 12))
    fig.suptitle(f'Parameter Impact Analysis - {image_name}', fontsize=16, fontweight='bold')

    # 1a. Quality metrics by quantization
    quantization_impact = df.groupby('quantization')[['MSE', 'SSIM', 'Histogram_Correlation', 'Edge_Similarity']].mean()

    quality_metrics = ['SSIM', 'Histogram_Correlation', 'Edge_Similarity']
    quantization_impact[quality_metrics].plot(kind='bar', ax=axes[0,0])
    axes[0,0].set_title('Quality Metrics by Quantization')
    axes[0,0].set_xlabel('Quantization')
    axes[0,0].set_ylabel('Metric Value')
    axes[0,0].legend()
    axes[0,0].tick_params(axis='x', rotation=0)
    axes[0,0].grid(True, alpha=0.3)

    # 1b. MSE by quantization (separate subplot)
    quantization_impact['MSE'].plot(kind='bar', ax=axes[0,1], color='red')
    axes[0,1].set_title('MSE by Quantization')
    axes[0,1].set_xlabel('Quantization')
    axes[0,1].set_ylabel('MSE')
    axes[0,1].tick_params(axis='x', rotation=0)
    axes[0,1].grid(True, alpha=0.3)

    # 2. Size ratio impact (max/min)
    df['size_ratio'] = df['max_size'] / df['min_size']
    size_ratio_impact = df.groupby('size_ratio')['Overall_Quality'].mean()
    size_ratio_impact.plot(kind='bar', ax=axes[0,2], color='skyblue')
    axes[0,2].set_title('Overall Quality by Size Ratio (Max/Min)')
    axes[0,2].set_xlabel('Size Ratio')
    axes[0,2].set_ylabel('Overall Quality')
    axes[0,2].tick_params(axis='x', rotation=45)
    axes[0,2].grid(True, alpha=0.3)

    # 3. Subdivision threshold impact
    sub_impact = df.groupby('sub_threshold')['Overall_Quality'].mean()
    sub_impact.plot(kind='bar', ax=axes[1,0], color='lightcoral')
    axes[1,0].set_title('Overall Quality by Subdivision Threshold')
    axes[1,0].set_xlabel('Subdivision Threshold')
    axes[1,0].set_ylabel('Overall Quality')
    axes[1,0].tick_params(axis='x', rotation=0)
    axes[1,0].grid(True, alpha=0.3)

    # 4a. Quality metrics by tile library
    tile_performance = df.groupby('tile_library')[['MSE', 'SSIM', 'Histogram_Correlation', 'Edge_Similarity']].mean()
    quality_metrics = ['SSIM', 'Histogram_Correlation', 'Edge_Similarity']
    tile_performance[quality_metrics].plot(kind='bar', ax=axes[1,1])
    axes[1,1].set_title('Quality Metrics by Tile Library')
    axes[1,1].set_xlabel('Tile Library')
    axes[1,1].set_ylabel('Metric Value')
    axes[1,1].legend()
    axes[1,1].tick_params(axis='x', rotation=45)
    axes[1,1].grid(True, alpha=0.3)

    # 4b. MSE by tile library
    tile_performance['MSE'].plot(kind='bar', ax=axes[1,2], color='red')
    axes[1,2].set_title('MSE by Tile Library')
    axes[1,2].set_xlabel('Tile Library')
    axes[1,2].set_ylabel('MSE')
    axes[1,2].tick_params(axis='x', rotation=45)
    axes[1,2].grid(True, alpha=0.3)

    plt.tight_layout()
    output_path = OUTPUT_DIR / image_name / 'parameter_impact.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved parameter impact to {output_path}")

def analyze_parameter_degradation(df, image_name):
    """Analyze which parameters cause quality degradation (for artistic/mosaic effects)"""
    print(f"[INFO] Analyzing parameter degradation effects for {image_name}")

    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle(f'Parameter Degradation Analysis (For Artistic Effects) - {image_name}', fontsize=16, fontweight='bold')

    # 1. Which parameters INCREASE MSE (worse quality = more artistic)
    mse_by_param = {}
    mse_by_param['quantization'] = df.groupby('quantization')['MSE'].mean()
    mse_by_param['min_size'] = df.groupby('min_size')['MSE'].mean()
    mse_by_param['max_size'] = df.groupby('max_size')['MSE'].mean()
    mse_by_param['sub_threshold'] = df.groupby('sub_threshold')['MSE'].mean()

    # Plot MSE increase trends
    ax = axes[0,0]
    colors = ['red', 'orange', 'green', 'blue']
    for i, (param, values) in enumerate(mse_by_param.items()):
        ax.plot(values.index, values.values, marker='o', label=param, color=colors[i], linewidth=2)

    ax.set_title('MSE by Parameters (Higher = More Artistic)')
    ax.set_xlabel('Parameter Value')
    ax.set_ylabel('Average MSE')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 2. Which parameters DECREASE SSIM (worse similarity = more artistic)
    ssim_by_param = {}
    ssim_by_param['quantization'] = df.groupby('quantization')['SSIM'].mean()
    ssim_by_param['min_size'] = df.groupby('min_size')['SSIM'].mean()
    ssim_by_param['max_size'] = df.groupby('max_size')['SSIM'].mean()
    ssim_by_param['sub_threshold'] = df.groupby('sub_threshold')['SSIM'].mean()

    ax = axes[0,1]
    for i, (param, values) in enumerate(ssim_by_param.items()):
        ax.plot(values.index, values.values, marker='s', label=param, color=colors[i], linewidth=2)

    ax.set_title('SSIM by Parameters (Lower = More Artistic)')
    ax.set_xlabel('Parameter Value')
    ax.set_ylabel('Average SSIM')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 3. Parameter impact ranking for degradation
    degradation_impact = {}

    # Calculate impact as difference between max and min values (normalized)
    for param in ['quantization', 'min_size', 'max_size', 'sub_threshold']:
        mse_range = mse_by_param[param].max() - mse_by_param[param].min()
        ssim_range = ssim_by_param[param].max() - ssim_by_param[param].min()

        # Normalize by overall metric range
        mse_impact = mse_range / df['MSE'].std()
        ssim_impact = ssim_range / df['SSIM'].std()

        degradation_impact[param] = (mse_impact + ssim_impact) / 2

    # Plot simple impact ranking
    ax = axes[1,0]
    params = list(degradation_impact.keys())
    impacts = list(degradation_impact.values())

    ax.bar(params, impacts, color='lightcoral')
    ax.set_title('Parameter Impact on Quality Degradation')
    ax.set_xlabel('Parameters')
    ax.set_ylabel('Impact Score')
    ax.tick_params(axis='x', rotation=45)
    ax.grid(True, alpha=0.3)

    # Add score labels
    for i, (param, impact) in enumerate(zip(params, impacts)):
        ax.text(i, impact + 0.05, f'{impact:.2f}', ha='center', va='bottom')

    # 4. Empty subplot (remove recommendations)
    axes[1,1].axis('off')

    plt.tight_layout()
    output_path = OUTPUT_DIR / image_name / 'parameter_degradation.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved parameter degradation analysis to {output_path}")

def analyze_cifar_comparison(df, image_name):
    """Direct comparison between CIFAR-10 and CIFAR-100"""
    print(f"[INFO] Analyzing CIFAR-10 vs CIFAR-100 comparison for {image_name}")

    # Filter data for CIFAR comparisons only
    cifar_data = df[df['tile_library'].isin(['cifar-10', 'cifar-100'])].copy()

    if len(cifar_data) == 0:
        print(f"[WARNING] No CIFAR data found for {image_name}")
        return

    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle(f'CIFAR-10 vs CIFAR-100 Direct Comparison - {image_name}', fontsize=16, fontweight='bold')

    # 1a. Quality metrics comparison
    metrics = ['MSE', 'SSIM', 'Histogram_Correlation', 'Edge_Similarity']
    cifar_comparison = cifar_data.groupby('tile_library')[metrics].mean()

    quality_metrics = ['SSIM', 'Histogram_Correlation', 'Edge_Similarity']
    cifar_comparison[quality_metrics].T.plot(kind='bar', ax=axes[0,0], color=['lightblue', 'lightcoral'])
    axes[0,0].set_title('Quality Metrics Comparison')
    axes[0,0].set_ylabel('Metric Value')
    axes[0,0].legend(title='Tile Library')
    axes[0,0].tick_params(axis='x', rotation=45)
    axes[0,0].grid(True, alpha=0.3)

    # 1b. MSE comparison (separate subplot)
    cifar_comparison['MSE'].plot(kind='bar', ax=axes[0,1], color=['red', 'orange'])
    axes[0,1].set_title('MSE Comparison')
    axes[0,1].set_ylabel('MSE')
    axes[0,1].legend(['CIFAR-10', 'CIFAR-100'])
    axes[0,1].grid(True, alpha=0.3)

    # 2. Paired comparison for same parameter settings
    # Create parameter combination identifier
    cifar_data['param_combo'] = (cifar_data['min_size'].astype(str) + '_' +
                                 cifar_data['max_size'].astype(str) + '_' +
                                 cifar_data['sub_threshold'].astype(str) + '_' +
                                 cifar_data['quantization'].astype(str))

    # Find configurations that exist for both CIFAR types
    cifar10_combos = set(cifar_data[cifar_data['tile_library'] == 'cifar-10']['param_combo'])
    cifar100_combos = set(cifar_data[cifar_data['tile_library'] == 'cifar-100']['param_combo'])
    common_combos = cifar10_combos.intersection(cifar100_combos)

    paired_data = []
    for combo in common_combos:
        combo_data = cifar_data[cifar_data['param_combo'] == combo]
        if len(combo_data) == 2:  # Should have both CIFAR-10 and CIFAR-100
            cifar10_row = combo_data[combo_data['tile_library'] == 'cifar-10'].iloc[0]
            cifar100_row = combo_data[combo_data['tile_library'] == 'cifar-100'].iloc[0]

            paired_data.append({
                'combo': combo,
                'cifar10_ssim': cifar10_row['SSIM'],
                'cifar100_ssim': cifar100_row['SSIM'],
                'cifar10_mse': cifar10_row['MSE'],
                'cifar100_mse': cifar100_row['MSE'],
                'ssim_diff': cifar10_row['SSIM'] - cifar100_row['SSIM'],
                'mse_diff': cifar10_row['MSE'] - cifar100_row['MSE']
            })

    paired_df = pd.DataFrame(paired_data)

    # 2. Win/Loss analysis
    ax = axes[1,0]
    if len(paired_df) > 0:
        ssim_wins = (paired_df['ssim_diff'] > 0).sum()  # CIFAR-10 wins
        ssim_losses = (paired_df['ssim_diff'] < 0).sum()  # CIFAR-100 wins

        mse_wins = (paired_df['mse_diff'] < 0).sum()  # CIFAR-10 wins (lower MSE is better)
        mse_losses = (paired_df['mse_diff'] > 0).sum()  # CIFAR-100 wins

        categories = ['SSIM', 'MSE']
        cifar10_scores = [ssim_wins, mse_wins]
        cifar100_scores = [ssim_losses, mse_losses]

        x = np.arange(len(categories))
        width = 0.35

        ax.bar(x - width/2, cifar10_scores, width, label='CIFAR-10 Wins', color='lightblue')
        ax.bar(x + width/2, cifar100_scores, width, label='CIFAR-100 Wins', color='lightcoral')

        ax.set_title(f'Win/Loss Analysis ({len(paired_df)} comparisons)')
        ax.set_ylabel('Number of Wins')
        ax.set_xticks(x)
        ax.set_xticklabels(categories)
        ax.legend()
        ax.grid(True, alpha=0.3)

        # Add value labels
        for i, (c10, c100) in enumerate(zip(cifar10_scores, cifar100_scores)):
            ax.text(i - width/2, c10 + 0.5, str(c10), ha='center', va='bottom')
            ax.text(i + width/2, c100 + 0.5, str(c100), ha='center', va='bottom')

    # 3. Performance difference distribution
    ax = axes[1,1]
    if len(paired_df) > 0:
        ax.hist(paired_df['ssim_diff'], bins=20, alpha=0.7, color='skyblue', edgecolor='black')
        ax.axvline(x=0, color='red', linestyle='--', linewidth=2)
        ax.set_title('SSIM Difference Distribution (CIFAR-10 - CIFAR-100)')
        ax.set_xlabel('SSIM Difference')
        ax.set_ylabel('Frequency')
        ax.grid(True, alpha=0.3)

    plt.tight_layout()
    output_path = OUTPUT_DIR / image_name / 'cifar_comparison.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved CIFAR comparison to {output_path}")

def analyze_size_consistency(all_dfs):
    """Analyze if min/max size effects are consistent across images"""
    print("[INFO] Analyzing min/max size consistency across images")

    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
    fig.suptitle('Min/Max Size Effects Consistency Across Images', fontsize=16, fontweight='bold')

    # Combine all data
    combined_data = []
    for image_name, df in all_dfs.items():
        df_copy = df.copy()
        df_copy['image'] = image_name
        combined_data.append(df_copy)

    combined_df = pd.concat(combined_data, ignore_index=True)

    # 1. Min size effect on SSIM across images
    ax = axes[0,0]
    for image in TARGET_IMAGES:
        image_data = combined_df[combined_df['image'] == image]
        min_size_effect = image_data.groupby('min_size')['SSIM'].mean()
        ax.plot(min_size_effect.index, min_size_effect.values, marker='o', label=image, linewidth=2)

    ax.set_title('Min Size Effect on SSIM')
    ax.set_xlabel('Min Size')
    ax.set_ylabel('Average SSIM')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 2. Max size effect on SSIM across images
    ax = axes[0,1]
    for image in TARGET_IMAGES:
        image_data = combined_df[combined_df['image'] == image]
        max_size_effect = image_data.groupby('max_size')['SSIM'].mean()
        ax.plot(max_size_effect.index, max_size_effect.values, marker='s', label=image, linewidth=2)

    ax.set_title('Max Size Effect on SSIM')
    ax.set_xlabel('Max Size')
    ax.set_ylabel('Average SSIM')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 3. Size ratio consistency
    combined_df['size_ratio'] = combined_df['max_size'] / combined_df['min_size']

    ax = axes[1,0]
    for image in TARGET_IMAGES:
        image_data = combined_df[combined_df['image'] == image]
        ratio_effect = image_data.groupby('size_ratio')['Overall_Quality'].mean()
        ax.plot(ratio_effect.index, ratio_effect.values, marker='^', label=image, linewidth=2)

    ax.set_title('Size Ratio Effect on Overall Quality')
    ax.set_xlabel('Size Ratio (Max/Min)')
    ax.set_ylabel('Average Overall Quality')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 4. Size ratio effect comparison
    ax = axes[1,1]

    # Show correlation values as a simple bar chart
    consistency_analysis = {}

    for size_param in ['min_size', 'max_size']:
        param_effects = {}
        for image in TARGET_IMAGES:
            image_data = combined_df[combined_df['image'] == image]
            effect = image_data.groupby(size_param)['SSIM'].mean()
            param_effects[image] = effect

        # Calculate correlation between images
        correlations = []
        images = list(param_effects.keys())
        for i in range(len(images)):
            for j in range(i+1, len(images)):
                # Find common parameter values
                common_params = set(param_effects[images[i]].index).intersection(
                    set(param_effects[images[j]].index)
                )
                if len(common_params) > 1:
                    vals_i = [param_effects[images[i]][p] for p in common_params]
                    vals_j = [param_effects[images[j]][p] for p in common_params]
                    corr = np.corrcoef(vals_i, vals_j)[0,1]
                    correlations.append(corr)

        consistency_analysis[size_param] = np.mean(correlations) if correlations else 0

    # Plot consistency as bar chart
    params = list(consistency_analysis.keys())
    correlations = list(consistency_analysis.values())

    ax.bar(params, correlations, color=['skyblue', 'lightgreen'])
    ax.set_title('Parameter Consistency Across Images')
    ax.set_xlabel('Size Parameters')
    ax.set_ylabel('Average Correlation')
    ax.set_ylim(0, 1)
    ax.grid(True, alpha=0.3)

    # Add correlation values
    for i, (param, corr) in enumerate(zip(params, correlations)):
        ax.text(i, corr + 0.02, f'{corr:.3f}', ha='center', va='bottom')

    plt.tight_layout()
    output_path = OUTPUT_DIR / 'size_consistency_analysis.png'
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"[INFO] Saved size consistency analysis to {output_path}")


def main():
    """Main analysis function"""
    print("=" * 60)
    print("MOSAIC PARAMETER BEHAVIOR ANALYSIS")
    print("=" * 60)

    setup_directories()

    # Load data
    all_dfs = load_metrics_data()

    if not all_dfs:
        print("[ERROR] No data loaded. Check metrics directory.")
        return

    # Analyze each image
    for image_name, df in all_dfs.items():
        print(f"\n[INFO] Analyzing {image_name}...")

        # Keep original heatmaps and parameter impact
        create_heatmaps(df, image_name)
        create_parameter_impact(df, image_name)

        # New analyses
        analyze_parameter_degradation(df, image_name)
        analyze_cifar_comparison(df, image_name)

    # Cross-image consistency analysis
    analyze_size_consistency(all_dfs)

    print("\n" + "=" * 60)
    print("ANALYSIS COMPLETE!")
    print("=" * 60)
    print(f"Results saved to: {OUTPUT_DIR}")
    print("\nGenerated analyses:")
    print("• heatmaps.png - Parameter impact heatmaps")
    print("• parameter_impact.png - Parameter effect analysis")
    print("• parameter_degradation.png - Settings for artistic effects")
    print("• cifar_comparison.png - CIFAR-10 vs CIFAR-100 comparison")
    print("• size_consistency_analysis.png - Cross-image size effect consistency")

if __name__ == "__main__":
    main()